HomeIoT ModelsResNeXt101Quantized

    ResNeXt101Quantized

    Imagenet classifier and general purpose backbone.

    ResNeXt101 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.

    TorchScriptTFLite
    2.91ms
    Inference Time
    0-3MB
    Memory Usage
    150NPU
    Layers

    Technical Details

    Model checkpoint:Imagenet
    Input resolution:224x224
    Number of parameters:88.7M
    Model size:87.3 MB

    Applicable Scenarios

    • Medical Imaging
    • Anomaly Detection
    • Inventory Management

    Licenses

    Source Model:BSD-3-CLAUSE
    Deployable Model:AI Model Hub License

    Tags

    • backbone
      A “backbone” model is designed to extract task-agnostic representations from specific data modalities (e.g., images, text, speech). This representation can then be fine-tuned for specialized tasks.
    • quantized
      A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.

    Supported IoT Devices

    • QCS6490 (Proxy)
    • QCS8250 (Proxy)
    • QCS8550 (Proxy)
    • RB3 Gen 2 (Proxy)
    • RB5 (Proxy)

    Supported IoT Chipsets

    • Qualcomm® QCS6490
    • Qualcomm® QCS8250
    • Qualcomm® QCS8550